In recent years, there has been a strong shift from hand-crafted regularizers towards data-driven reconstruction schemes.
The learning-based methods can be applied in the sinogram domain [1]}, [2]}, such that the final image can be reconstructed using traditional reconstruction algorithms.
Alternatively, a preliminary reconstruction may be computed using the (noisy and possibly incomplete) sinogram, which can subsequently be enhanced by a trained cnn [3]}.
An alternative learning-based reconstruction approach is to learn a direct mapping from the data domain to the image domain [4]}.
However, this requires to learn a wealth of parameters solely to compute an approximate inverse of the forward acquisition operator.
Another recently popularized approach is to learn an unrolled iterative reconstruction algorithm [5]}, [6]}, [7]}.
Whilst the results look promising, we point out that such approaches typically assume a particular acquisition setup and, at inference time, can only be applied in settings that are very similar to the training setting.
